Geroprotectors.org: a new, structured and curated database of current therapeutic interventions in aging and age-related disease

Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations

Accurate and physiologically meaningful biomarkers for human aging are key to assessing antiaging therapies. Given ethnic differences in health, diet, lifestyle, behavior, environmental exposures, and even average rate of biological aging, it stands to reason that aging clocks trained on datasets obtained from specific ethnic populations are more likely to account for these potential confounding factors, resulting in an enhanced capacity to predict chronological age and quantify biological age. Here, we present a deep learning-based hematological aging clock modeled using the large combined dataset of Canadian, South Korean, and Eastern European population blood samples that show increased predictive accuracy in individual populations compared to population specific hematologic aging clocks. The performance of models was also evaluated on publicly available samples of the American population from the National Health and Nutrition Examination Survey (NHANES). In addition, we explored the association between age predicted by both population specific and combined hematological clocks and all-cause mortality. Overall, this study suggests (a) the population specificity of aging patterns and (b) hematologic clocks predicts all-cause mortality. The proposed models were added to the freely-available Aging.AI system expanding the range of tools for analysis of human aging.

Gene expression-based drug repurposing to target aging

Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target‐centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug‐perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.

Effects of N-acetyl-L-cysteine on lifespan, locomotor activity and stress-resistance of 3 Drosophila species with different lifespans

N-acetyl-L-cysteine (NAC) is a derivative of the sulphur-containing amino acid L-cysteine with potential anti-aging properties. We studied 3 Drosophila species with contrast longevity differences (D. virilis is longest-lived, D. kikkawai is shortest-lived and D. melanogaster has moderate lifespan) to test the effects of NAC at 8 different concentrations (from 10 nM to 100 mM) on the lifespan, stress-resistance and locomotor activity. Except the adverse effects of highest (10 mM and 100 mM) concentrations NAC demonstrated sexually opposite and male-biased effects on Drosophila lifespan, stress-resistance and locomotor activity and not satisfied the criteria of a geroprotector in terms of the reproducibility of lifespan extending effects in different model organisms. The concentration- and sex-dependent changes in the relative expression levels of the antioxidant genes (Cat/CG6871 and Sod1/CG11793) and genes involved in hydrogen sulfide biosynthesis (Cbs/CG1753, Eip55E/CG5345 and Nfs1/CG12264) suggest the involvement of hormetic mechanisms in the geroprotective effects of NAC.

Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification

For the past several decades, research in understanding the molecular basis of human muscle aging has progressed significantly. However, the development of accessible tissue-specific biomarkers of human muscle aging that may be measured to evaluate the effectiveness of therapeutic interventions is still a major challenge. Here we present a method for tracking age-related changes of human skeletal muscle. We analyzed publicly available gene expression profiles of young and old tissue from healthy donors. Differential gene expression and pathway analysis were performed to compare signatures of young and old muscle tissue and to preprocess the resulting data for a set of machine learning algorithms. Our study confirms the established mechanisms of human skeletal muscle aging, including dysregulation of cytosolic Ca²⁺ homeostasis, PPAR signaling and neurotransmitter recycling along with IGFR and PI3K-Akt-mTOR signaling. Applying several supervised machine learning techniques, including neural networks, we built a panel of tissue-specific biomarkers of aging. Our predictive model achieved 0.91 Pearson correlation with respect to the actual age values of the muscle tissue samples, and a mean absolute error of 6.19 years on the test set. The performance of models was also evaluated on gene expression samples of the skeletal muscles from the Gene expression Genotype-Tissue Expression (GTEx) project. The best model achieved the accuracy of 0.80 with respect to the actual age bin prediction on the external validation set. Furthermore, we demonstrated that aging biomarkers can be used to identify new molecular targets for tissue-specific anti-aging therapies.

MortalityPredictors.org: a manually-curated database of published biomarkers of human all-cause mortality

Biomarkers of all-cause mortality are of tremendous clinical and research interest. Because of the long potential duration of prospective human lifespan studies, such biomarkers can play a key role in quantifying human aging and quickly evaluating any potential therapies. Decades of research into mortality biomarkers have resulted in numerous associations documented across hundreds of publications. Here, we present MortalityPredictors.org, a manually-curated, publicly accessible database, housing published, statistically-significant relationships between biomarkers and all-cause mortality in population-based or generally healthy samples. To gather the information for this database, we searched PubMed for appropriate research papers and then manually curated relevant data from each paper. We manually curated 1,576 biomarker associations, involving 471 distinct biomarkers. Biomarkers ranged in type from hematologic (red blood cell distribution width) to molecular (DNA methylation changes) to physical (grip strength). Via the web interface, the resulting data can be easily browsed, searched, and downloaded for further analysis. MortalityPredictors.org provides comprehensive results on published biomarkers of human all-cause mortality that can be used to compare biomarkers, facilitate meta-analysis, assist with the experimental design of aging studies, and serve as a central resource for analysis. We hope that it will facilitate future research into human mortality and aging.

Vive la radiorésistance!: converging research in radiobiology and biogerontology to enhance human radioresistance for deep space exploration and colonization

While many efforts have been made to pave the way toward human space colonization, little consideration has been given to the methods of protecting spacefarers against harsh cosmic and local radioactive environments and the high costs associated with protection from the deleterious physiological effects of exposure to high-Linear energy transfer (high-LET) radiation. Herein, we lay the foundations of a roadmap toward enhancing human radioresistance for the purposes of deep space colonization and exploration. We outline future research directions toward the goal of enhancing human radioresistance, including upregulation of endogenous repair and radioprotective mechanisms, possible leeways into gene therapy in order to enhance radioresistance via the translation of exogenous and engineered DNA repair and radioprotective mechanisms, the substitution of organic molecules with fortified isoforms, and methods of slowing metabolic activity while preserving cognitive function. We conclude by presenting the known associations between radioresistance and longevity, and articulating the position that enhancing human radioresistance is likely to extend the healthspan of human spacefarers as well.

Six plant extracts delay yeast chronological aging through different signaling pathways

Our recent study has revealed six plant extracts that slow yeast chronological aging more efficiently than any chemical compound yet described. The rate of aging in yeast is controlled by an evolutionarily conserved network of integrated signaling pathways and protein kinases. Here, we assessed how single-gene-deletion mutations eliminating each of these pathways and kinases affect the aging-delaying efficiencies of the six plant extracts. Our findings imply that these extracts slow aging in the following ways: 1) plant extract 4 decreases the efficiency with which the pro-aging TORC1 pathway inhibits the anti-aging SNF1 pathway; 2) plant extract 5 mitigates two different branches of the pro-aging PKA pathway; 3) plant extract 6 coordinates processes that are not assimilated into the network of presently known signaling pathways/protein kinases; 4) plant extract 8 diminishes the inhibitory action of PKA on SNF1; 5) plant extract 12 intensifies the anti-aging protein kinase Rim15; and 6) plant extract 21 inhibits a form of the pro-aging protein kinase Sch9 that is activated by the pro-aging PKH1/2 pathway.

Tissue repair genes: the TiRe database and its implication for skin wound healing

Wound healing is an inherent feature of any multicellular organism and recent years have brought about a huge amount of data regarding regular and abnormal tissue repair. Despite the accumulated knowledge, modulation of wound healing is still a major biomedical challenge, especially in advanced ages. In order to collect and systematically organize what we know about the key players in wound healing, we created the TiRe (Tissue Repair) database, an online collection of genes and proteins that were shown to directly affect skin wound healing. To date, TiRe contains 397 entries for four organisms: Mus musculus, Rattus norvegicus, Sus domesticus, and Homo sapiens. Analysis of the TiRe dataset of skin wound healing-associated genes showed that skin wound healing genes are (i) over-conserved among vertebrates, but are under-conserved in invertebrates; (ii) enriched in extracellular and immuno-inflammatory genes; and display (iii) high interconnectivity and connectivity to other proteins. The latter may provide potential therapeutic targets. In addition, a slower or faster skin wound healing is indicative of an aging or longevity phenotype only when assessed in advanced ages, but not in the young. In the long run, we aim for TiRe to be a one-station resource that provides researchers and clinicians with the essential data needed for a better understanding of the mechanisms of wound healing, designing new experiments, and the development of new therapeutic strategies. TiRe is freely available online at http://www.tiredb.org.

Deep biomarkers of human aging: Application of deep neural networks to biomarker development

One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r = 0.90 with R² = 0.80 and MAE = 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r = 0.91 with R² = 0.82 and MAE = 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. To allow for public testing and evaluate real-life performance of the predictor, we developed an online system available at http://www.aging.ai. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.

In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state

Populations in developed nations throughout the world are rapidly aging, and the search for geroprotectors, or anti-aging interventions, has never been more important. Yet while hundreds of geroprotectors have extended lifespan in animal models, none have yet been approved for widespread use in humans. GeroScope is a computational tool that can aid prediction of novel geroprotectors from existing human gene expression data. GeroScope maps expression differences between samples from young and old subjects to aging-related signaling pathways, then profiles pathway activation strength (PAS) for each condition. Known substances are then screened and ranked for those most likely to target differential pathways and mimic the young signalome. Here we used GeroScope and shortlisted ten substances, all of which have lifespan-extending effects in animal models, and tested 6 of them for geroprotective effects in senescent human fibroblast cultures. PD-98059, a highly selective MEK1 inhibitor, showed both life-prolonging and rejuvenating effects. Natural compounds like N-acetyl-L-cysteine, Myricetin and Epigallocatechin gallate also improved several senescence-associated properties and were further investigated with pathway analysis. This work not only highlights several potential geroprotectors for further study, but also serves as a proof-of-concept for GeroScope, Oncofinder and other PAS-based methods in streamlining drug prediction, repurposing and personalized medicine.

Towards natural mimetics of metformin and rapamycin

Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals-safer, naturally-occurring compounds-that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors.

Machine learning for predicting lifespan-extending chemical compounds

Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age-related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenorhabditis elegans' lifespan, using as features Gene Ontology (GO) terms annotated for proteins targeted by the compounds and chemical descriptors calculated from each compound's chemical structure. The model with the best predictive accuracy used both biological and chemical features, achieving a prediction accuracy of 80%. The top 20 most important GO terms include those related to mitochondrial processes, to enzymatic and immunological processes, and terms related to metabolic and transport processes. We applied our best model to predict compounds which are more likely to increase C. elegans' lifespan in the DGIdb database, where the effect of the compounds on an organism's lifespan is unknown. The top hit compounds can be broadly divided into four groups: compounds affecting mitochondria, compounds for cancer treatment, anti-inflammatories, and compounds for gonadotropin-releasing hormone therapies.

Chemo brain: From discerning mechanisms to lifting the brain fog-An aging connection

Mounting evidence indicates that cancer treatments cause numerous deleterious effects, including central nervous system (CNS) toxicity. Chemotherapy-caused CNS side effects encompass changes in cognitive function, memory, and attention, to name a few. Although chemotherapy treatment-induced side effects occur in 16-75% of all patients, the mechanisms of these effects are not well understood. We have recently proposed a new epigenetic theory of chemo brain and, in a pioneer study, determined that cytotoxic chemotherapy agents induce oxidative DNA damage and affect molecular and epigenetic processes in the brain, and may be associated with brain aging processes. In this paper, we discuss the implications of chemo brain epigenetic effects and future perspectives, as well as outline potential links with brain aging and future translational research opportunities.

The DrugAge database of aging-related drugs

Aging is a major worldwide medical challenge. Not surprisingly, identifying drugs and compounds that extend lifespan in model organisms is a growing research area. Here, we present DrugAge (http://genomics.senescence.info/drugs/), a curated database of lifespan‐extending drugs and compounds. At the time of writing, DrugAge contains 1316 entries featuring 418 different compounds from studies across 27 model organisms, including worms, flies, yeast and mice. Data were manually curated from 324 publications. Using drug–gene interaction data, we also performed a functional enrichment analysis of targets of lifespan‐extending drugs. Enriched terms include various functional categories related to glutathione and antioxidant activity, ion transport and metabolic processes. In addition, we found a modest but significant overlap between targets of lifespan‐extending drugs and known aging‐related genes, suggesting that some but not most aging‐related pathways have been targeted pharmacologically in longevity studies. DrugAge is freely available online for the scientific community and will be an important resource for biogerontologists.

Mechanisms Underlying the Essential Role of Mitochondrial Membrane Lipids in Yeast Chronological Aging

The functional state of mitochondria is vital to cellular and organismal aging in eukaryotes across phyla. Studies in the yeast Saccharomyces cerevisiae have provided evidence that age-related changes in some aspects of mitochondrial functionality can create certain molecular signals. These signals can then define the rate of cellular aging by altering unidirectional and bidirectional communications between mitochondria and other organelles. Several aspects of mitochondrial functionality are known to impact the replicative and/or chronological modes of yeast aging. They include mitochondrial electron transport, membrane potential, reactive oxygen species, and protein synthesis and proteostasis, as well as mitochondrial synthesis of iron-sulfur clusters, amino acids, and NADPH. Our recent findings have revealed that the composition of mitochondrial membrane lipids is one of the key aspects of mitochondrial functionality affecting yeast chronological aging. We demonstrated that exogenously added lithocholic bile acid can delay chronological aging in yeast because it elicits specific changes in mitochondrial membrane lipids. These changes allow mitochondria to operate as signaling platforms that delay yeast chronological aging by orchestrating an institution and maintenance of a distinct cellular pattern. In this review, we discuss molecular and cellular mechanisms underlying the essential role of mitochondrial membrane lipids in yeast chronological aging.

Geroprotectors: A Unified Concept and Screening Approaches

Although the geroprotectors discovery is a new biomedicine trend and more than 200 compounds can slow aging and increase the lifespan of the model organism, there are still no geroprotectors on the market. The reasons may be partly related to the lack of a unified concept of geroprotector, accepted by the scientific community. Such concept as a system of criteria for geroprotector identification and classification can form a basis for an analytical model of anti-aging drugs, help to consolidate the efforts of various research initiatives in this area and compare their results. Here, we review the existing classification and characteristics of geroprotectors based on their effect on the survival of a group of individuals or pharmaceutics classes, according to the proposed mechanism of their geroprotective action or theories of aging. After discussing advantages and disadvantages of these approaches, we offer a new concept based on the maintenance of homeostatic capacity because aging can be considered as exponential shrinkage of homeostatic capacity leading to the onset of age-related diseases and death. Besides, we review the most promising current screening approaches to finding new geroprotectors. Establishing the classification of existing geroprotectors based on physiology and current understanding of the nature of aging is essential for putting the existing knowledge into a single system. This system could be useful to formulate standards for finding and creating new geroprotectors. Standardization, in turn, would allow easier comparison and combination of experimental data obtained by different research groups.

Therapeutic Manipulation of Ageing: Repurposing Old Dogs and Discovering New Tricks

Ageing is a leading risk factor for many debilitating diseases. While age-related diseases have been the subject of over a century of intense investigation, until recently, physiological ageing was considered unavoidable. Pharmacological and genetic studies have since shown that ageing is a malleable process and that its abrogation can prevent its associated diseases. This review summarises a sample of the most promising efforts to deliver the products of ageing research to the clinic. Current efforts include the use of clinically approved drugs that have since been repurposed, as well as the development of novel therapeutics, to target ageing. Furthermore, ongoing research has sought reliable biomarkers of ageing that will accelerate the development of such therapeutics. Development of these technologies will improve quality of late-life and help relieve the enormous stress placed on state healthcare systems by a rapidly ageing global population. Thus, for both medical and socioeconomic reasons, it is imperative that ageing is made to yield to intervention.

Deciphering hallmark processes of aging from interaction networks

BACKGROUND

Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death.

SCOPE OF REVIEW

The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors.

MAJOR CONCLUSIONS

Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging.

GENERAL SIGNIFICANCE

In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Developing criteria for evaluation of geroprotectors as a key stage toward translation to the clinic

In the coming decades, a massive shift in the aging segment of the population will have major social and economic consequences around the world. One way to offset this increase is to expedite the development of geroprotectors, substances that slow aging, repair age‐associated damage and extend healthy lifespan, or healthspan. While over 200 geroprotectors are now reported in model organisms and some are in human use for specific disease indications, the path toward determining whether they affect aging in humans remains obscure. Translation to the clinic is hampered by multiple issues including absence of a common set of criteria to define, select, and classify these substances, given the complexity of the aging process and their enormous diversity in mechanism of action. Translational research efforts would benefit from the formation of a scientific consensus on the following: the definition of ‘geroprotector’, the selection criteria for geroprotectors, a comprehensive classification system, and an analytical model. Here, we review current approaches to selection and put forth our own suggested selection criteria. Standardizing selection of geroprotectors will streamline discovery and analysis of new candidates, saving time and cost involved in translation to clinic.